College Admission Logistic Regression

This project demonstrates the application of logistic regression to predict college admissions based on student scores. It showcases the setup and execution of a machine learning model in a Google Colab environment using Python's sklearn library.

Table of Contents

• Project Overview
• Features
• Data Description
• Installation
• Usage
• Contributing
• License
• Contact

Project Overview

The "College Admission Logistic Regression" project predicts student admissions utilizing logistic regression techniques based on standardized test scores from a dataset. The project demonstrates comprehensive steps from data loading and preprocessing to model training and evaluation.

Features

• Data Loading and Preparation: Seamless integration with Google Colab for data handling.
• Model Training: Utilization of logistic regression for predictive analysis.
• Performance Evaluation: Metrics such as precision, recall, and F-score are used to evaluate the model's effectiveness.

Data Description

The dataset primarily includes:

• CET_score: The score obtained by students.
• Admitted: Binary indicator (1 for admitted, 0 for not admitted).

Installation

Set up the project environment using Google Colab for efficient execution:

from google.colab import drive
drive.mount('/content/drive')

Clone the repository:

git clone https://github.com/ascender1729/CollegeAdmissionLogisticRegression.git
cd CollegeAdmissionLogisticRegression

Usage

Before running the notebook, install the required Python libraries. Execute the following commands to ensure all dependencies are installed:

pip install pandas numpy matplotlib seaborn scikit-learn

The libraries you will use in this project include:

• pandas: For data manipulation and analysis.
• numpy: To work with arrays and perform mathematical operations.
• matplotlib.pyplot: For creating static, animated, and interactive visualizations in Python.
• seaborn: A Python data visualization library based on matplotlib, providing a high-level interface for drawing attractive statistical graphics.
• sklearn.model_selection: Specifically train_test_split, to split the data into training and testing sets.
• sklearn.linear_model: Specifically LogisticRegression, to perform the logistic regression analysis.
• sklearn.metrics: Includes classification_report, confusion_matrix, and precision_recall_fscore_support, for model evaluation.

Execute the notebook within Google Colab to follow the detailed steps from data preprocessing to model evaluation.

Contributing

Contributions to enhance the model or improve methodologies are highly appreciated. Please fork the repository and submit pull requests for review.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Pavan Kumar - pavankumard.pg19.ma@nitp.ac.in

LinkedIn: @ascender1729

Project Link: College Admission Logistic Regression